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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/quick_start.html">Quick Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="../getting_started/capture_and_replay.html">Introduction</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../tutorials/notebooks.html">Legacy notebooks</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="torch_tensorrt.html">torch_tensorrt</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">torch_tensorrt.dynamo</a></li>
<li class="toctree-l1"><a class="reference internal" href="logging.html">torch_tensorrt.logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="fx.html">torch_tensorrt.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="ts.html">torch_tensorrt.ts</a></li>
<li class="toctree-l1"><a class="reference internal" href="ptq.html">torch_tensorrt.ts.ptq</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../contributors/system_overview.html">System Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
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  <section id="torch-tensorrt-dynamo">
<span id="torch-tensorrt-dynamo-py"></span><h1>torch_tensorrt.dynamo<a class="headerlink" href="#torch-tensorrt-dynamo" title="Permalink to this heading">¶</a></h1>
<span class="target" id="module-torch_tensorrt.dynamo"></span><section id="functions">
<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">¶</a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.compile">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exported_program</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ExportedProgram</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwarg_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt._Device.Device"><span class="pre">Device</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">device</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_dynamic_shape_support</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Set</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">dtype</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.dtype" title="torch_tensorrt._enums.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">dtype</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.dtype" title="torch_tensorrt._enums.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{dtype.f32}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">engine_capability</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt._enums.EngineCapability"><span class="pre">EngineCapability</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">EngineCapability.STANDARD</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_double</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Collection</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_modules</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pass_through_build_failures</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_aux_streams</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version_compatible</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_python_runtime</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_fast_partitioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_experimental_decompositions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dryrun</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hardware_compatible</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timing_cache_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'/tmp/torch_tensorrt_engine_cache/timing_cache.bin'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lazy_engine_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_built_engines</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reuse_cached_engines</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">engine_cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'/tmp/torch_tensorrt_engine_cache'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">engine_cache_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5368709120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_engine_cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEngineCache</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_explicit_typing</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_fp32_acc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit_identical_engine_weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strip_engine_weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">immutable_weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_weight_streaming</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tiling_optimization_level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'none'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l2_limit_for_tiling</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_module_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_distributed_mode_trace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">GraphModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.compile" title="Permalink to this definition">¶</a></dt>
<dd><p>Compile an ExportedProgram module for NVIDIA GPUs using TensorRT</p>
<p>Takes a existing TorchScript module and a set of settings to configure the compiler
and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
<p>Converts specifically the forward method of a TorchScript Module</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>exported_program</strong> (<em>torch.export.ExportedProgram</em>) – Source module, running torch.export on a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
<li><p><strong>inputs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Sequence</em><em>[</em><em>Any</em><em>]</em><em>]</em><em>]</em>) – <p>List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type.</p>
<blockquote>
<div><div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span><span class="o">=</span><span class="p">[</span>
    <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)),</span> <span class="c1"># Static NCHW input shape for input #1</span>
    <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span>
        <span class="n">min_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
        <span class="n">opt_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
        <span class="n">max_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span>
        <span class="nb">format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channel_last</span>
    <span class="p">),</span> <span class="c1"># Dynamic input shape for input #2</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">244</span><span class="p">))</span> <span class="c1"># Use an example tensor and let torch_tensorrt infer settings</span>
<span class="p">]</span>
</pre></div>
</div>
</div></blockquote>
</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>arg_inputs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Sequence</em><em>[</em><em>Any</em><em>]</em><em>]</em><em>]</em>) – Same as inputs. Alias for better understanding with kwarg_inputs.</p></li>
<li><p><strong>kwarg_inputs</strong> (<em>Optional</em><em>[</em><em>dict</em><em>[</em><em>Any</em><em>, </em><em>Any</em><em>]</em><em>]</em>) – kwarg inputs to the module forward function.</p></li>
<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span>
</pre></div>
</div>
</p></li>
<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
<li><p><strong>assume_dynamic_shape_support</strong> (<em>bool</em>) – Setting this to true enables the converters work for both dynamic and static shapes. Default: False</p></li>
<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
<li><p><strong>enabled_precisions</strong> (<em>Union</em><em>[</em><em>Set</em><em>[</em><em>Union</em><em>[</em><em>torch.dpython:type</em><em>, </em><em>dpython:type</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>Union</em><em>[</em><em>torch.dpython:type</em><em>, </em><em>dpython:type</em><em>]</em><em>]</em><em>]</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
<li><p><strong>engine_capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
<li><p><strong>truncate_double</strong> (<em>bool</em>) – Truncate weights provided in double (float64) to float32</p></li>
<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</p></li>
<li><p><strong>min_block_size</strong> (<em>python:int</em>) – The minimum number of contiguous TensorRT convertible operations in order to run a set of operations in TensorRT</p></li>
<li><p><strong>torch_executed_ops</strong> (<em>Optional</em><em>[</em><em>Collection</em><em>[</em><em>Target</em><em>]</em><em>]</em>) – Set of aten operators that must be run in PyTorch. An error will be thrown if this set is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
<li><p><strong>torch_executed_modules</strong> (<em>Optional</em><em>[</em><em>List</em><em>[</em><em>str</em><em>]</em><em>]</em>) – List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
<li><p><strong>pass_through_build_failures</strong> (<em>bool</em>) – Error out if there are issues during compilation (only applicable to torch.compile workflows)</p></li>
<li><p><strong>max_aux_streams</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Maximum streams in the engine</p></li>
<li><p><strong>version_compatible</strong> (<em>bool</em>) – Build the TensorRT engines compatible with future versions of TensorRT (Restrict to lean runtime operators to provide version forward compatibility for the engines)</p></li>
<li><p><strong>optimization_level</strong> – (Optional[int]): Setting a higher optimization level allows TensorRT to spend longer engine building time searching for more optimization options. The resulting engine may have better performance compared to an engine built with a lower optimization level. The default optimization level is 3. Valid values include integers from 0 to the maximum optimization level, which is currently 5. Setting it to be greater than the maximum level results in identical behavior to the maximum level.</p></li>
<li><p><strong>use_python_runtime</strong> – (bool): Return a graph using a pure Python runtime, reduces options for serialization</p></li>
<li><p><strong>use_fast_partitioner</strong> – (bool): Use the adjacency based partitioning scheme instead of the global partitioner. Adjacency partitioning is faster but may not be optimal. Use the global paritioner (<code class="docutils literal notranslate"><span class="pre">False</span></code>) if looking for best performance</p></li>
<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Use the full set of operator decompositions. These decompositions may not be tested but serve to make the graph easier to convert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</p></li>
<li><p><strong>dryrun</strong> (<em>bool</em>) – Toggle for “Dryrun” mode, running everything except conversion to TRT and logging outputs</p></li>
<li><p><strong>hardware_compatible</strong> (<em>bool</em>) – Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)</p></li>
<li><p><strong>timing_cache_path</strong> (<em>str</em>) – Path to the timing cache if it exists (or) where it will be saved after compilation</p></li>
<li><p><strong>lazy_engine_init</strong> (<em>bool</em>) – Defer setting up engines until the compilation of all engines is complete. Can allow larger models with multiple graph breaks to compile but can lead to oversubscription of GPU memory at runtime.</p></li>
<li><p><strong>cache_built_engines</strong> (<em>bool</em>) – Whether to save the compiled TRT engines to storage</p></li>
<li><p><strong>reuse_cached_engines</strong> (<em>bool</em>) – Whether to load the compiled TRT engines from storage</p></li>
<li><p><strong>engine_cache_dir</strong> (<em>str</em>) – Directory to store the cached TRT engines</p></li>
<li><p><strong>engine_cache_size</strong> (<em>python:int</em>) – Maximum hard-disk space (bytes) to use for the engine cache, default is 1GB. If the cache exceeds this size, the oldest engines will be removed by default</p></li>
<li><p><strong>custom_engine_cache</strong> (<em>Optional</em><em>[</em><em>BaseEngineCache</em><em>]</em>) – Engine cache instance to use for saving and loading engines. Users can provide their own engine cache by inheriting from BaseEngineCache. If used, engine_cache_dir and engine_cache_size will be ignored.</p></li>
<li><p><strong>use_explicit_typing</strong> (<em>bool</em>) – This flag enables strong typing in TensorRT compilation which respects the precisions set in the Pytorch model. This is useful when users have mixed precision graphs.</p></li>
<li><p><strong>use_fp32_acc</strong> (<em>bool</em>) – This option inserts cast to FP32 nodes around matmul layers and TensorRT ensures the accumulation of matmul happens in FP32. Use this only when FP16 precision is configured in enabled_precisions.</p></li>
<li><p><strong>refit_identical_engine_weights</strong> (<em>bool</em>) – Refit engines with identical weights. This is useful when the same model is compiled multiple times with different inputs and the weights are the same. This will save time by reusing the same engine for different inputs.</p></li>
<li><p><strong>strip_engine_weights</strong> (<em>bool</em>) – Strip engine weights from the serialized engine. This is useful when the engine is to be deployed in an environment where the weights are not required.</p></li>
<li><p><strong>immutable_weights</strong> (<em>bool</em>) – Build non-refittable engines. This is useful for some layers that are not refittable. If this argument is set to true, <cite>strip_engine_weights</cite> and <cite>refit_identical_engine_weights</cite> will be ignored.</p></li>
<li><p><strong>enable_weight_streaming</strong> (<em>bool</em>) – Enable weight streaming.</p></li>
<li><p><strong>tiling_optimization_level</strong> (<em>str</em>) – The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support [“none”, “fast”, “moderate”, “full”].</p></li>
<li><p><strong>l2_limit_for_tiling</strong> (<em>python:int</em>) – The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).</p></li>
<li><p><strong>offload_module_to_cpu</strong> (<em>bool</em>) – Offload the module to CPU. This is useful when we need to minimize GPU memory usage.</p></li>
<li><p><strong>use_distributed_mode_trace</strong> (<em>bool</em>) – Using aot_autograd to trace the graph. This is enabled when DTensors or distributed tensors are present in distributed model</p></li>
<li><p><strong>**kwargs</strong> – Any,</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Compiled FX Module, when run it will execute via TensorRT</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>torch.fx.GraphModule</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.trace">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.nn.modules.module.Module</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">torch.fx.graph_module.GraphModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwarg_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ExportedProgram</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_tracer.html#trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.trace" title="Permalink to this definition">¶</a></dt>
<dd><p>Exports a <code class="docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code> from a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code> specifically targeting being compiled with Torch-TensorRT</p>
<p>Exports a <code class="docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code> from either a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> or torch.fx.GraphModule``. Runs specific operator decompositions geared towards
compilation by Torch-TensorRT’s dynamo frontend.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mod</strong> (<em>torch.nn.Module</em><em> | </em><em>torch.fx.GraphModule</em>) – Source module to later be compiled by Torch-TensorRT’s dynamo fronted</p></li>
<li><p><strong>inputs</strong> (<em>Tuple</em><em>[</em><em>Any</em><em>, </em><em>...</em><em>]</em>) – <p>List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type.</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
    torch_tensorrt.Input(
        min_shape=(1, 224, 224, 3),
        opt_shape=(1, 512, 512, 3),
        max_shape=(1, 1024, 1024, 3),
        dtype=torch.int32
        format=torch.channel_last
    ), # Dynamic input shape for input #2
    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
]
</pre></div>
</div>
</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>arg_inputs</strong> (<em>Tuple</em><em>[</em><em>Any</em><em>, </em><em>...</em><em>]</em>) – Same as inputs. Alias for better understanding with kwarg_inputs.</p></li>
<li><p><strong>kwarg_inputs</strong> (<em>dict</em><em>[</em><em>Any</em><em>, </em><em>...</em><em>]</em>) – Optional, kwarg inputs to the module forward function.</p></li>
<li><p><strong>device</strong> (<em>Union</em><em>(</em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="p">.</span><span class="n">device</span><span class="p">(</span><span class="s">&quot;cuda:0&quot;</span><span class="p">)</span>
</pre></div>
</div>
</p></li>
<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Use the full set of operator decompositions. These decompositions may not be tested but serve to make the graph easier to convert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</p></li>
<li><p><strong>**kwargs</strong> – Any,</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Compiled FX Module, when run it will execute via TensorRT</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>torch.fx.GraphModule</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.export">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">GraphModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cross_compile_module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ExportedProgram</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_exporter.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.export" title="Permalink to this definition">¶</a></dt>
<dd><p>Export the result of TensorRT compilation into the desired output format.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>gm</strong> (<em>torch.fx.GraphModule</em>) – Compiled Torch-TensorRT module, generated by <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code></p></li>
<li><p><strong>inputs</strong> (<em>torch.Tensor</em>) – Torch input tensors</p></li>
<li><p><strong>cross_compile_module</strong> (<em>bool</em>) – Flag to indicated whether it is cross_compilation enabled or not</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.convert_exported_program_to_serialized_trt_engine">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">convert_exported_program_to_serialized_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exported_program</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ExportedProgram</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwarg_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt._Device.Device"><span class="pre">Device</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">device</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_dynamic_shape_support</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Set</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">dtype</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.dtype" title="torch_tensorrt._enums.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">dtype</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.dtype" title="torch_tensorrt._enums.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{dtype.f32}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">engine_capability</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt._enums.EngineCapability"><span class="pre">EngineCapability</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">EngineCapability.STANDARD</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_double</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Collection</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_modules</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pass_through_build_failures</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_aux_streams</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version_compatible</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_python_runtime</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_fast_partitioner</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_experimental_decompositions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dryrun</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hardware_compatible</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timing_cache_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'/tmp/torch_tensorrt_engine_cache/timing_cache.bin'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lazy_engine_init</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_built_engines</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reuse_cached_engines</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">engine_cache_dir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'/tmp/torch_tensorrt_engine_cache'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">engine_cache_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">5368709120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_engine_cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEngineCache</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_explicit_typing</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_fp32_acc</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit_identical_engine_weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strip_engine_weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">immutable_weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_weight_streaming</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tiling_optimization_level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'none'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">l2_limit_for_tiling</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_module_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_distributed_mode_trace</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bytes</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_compiler.html#convert_exported_program_to_serialized_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.convert_exported_program_to_serialized_trt_engine" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert an ExportedProgram to a serialized TensorRT engine</p>
<p>Converts an ExportedProgram to a serialized TensorRT engine given a dictionary of conversion settings</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>exported_program</strong> (<em>torch.export.ExportedProgram</em>) – Source module, running torch.export on a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
<li><p><strong>inputs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Sequence</em><em>[</em><em>Any</em><em>]</em><em>]</em><em>]</em>) – <p>List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
to select device type.</p>
<blockquote>
<div><div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span><span class="o">=</span><span class="p">[</span>
    <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)),</span> <span class="c1"># Static NCHW input shape for input #1</span>
    <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span>
        <span class="n">min_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
        <span class="n">opt_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
        <span class="n">max_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span>
        <span class="nb">format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channel_last</span>
    <span class="p">),</span> <span class="c1"># Dynamic input shape for input #2</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">244</span><span class="p">))</span> <span class="c1"># Use an example tensor and let torch_tensorrt infer settings</span>
<span class="p">]</span>
</pre></div>
</div>
</div></blockquote>
</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>arg_inputs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Sequence</em><em>[</em><em>Any</em><em>]</em><em>]</em><em>]</em>) – Same as inputs. Alias for better understanding with kwarg_inputs.</p></li>
<li><p><strong>kwarg_inputs</strong> (<em>Optional</em><em>[</em><em>dict</em><em>[</em><em>Any</em><em>, </em><em>Any</em><em>]</em><em>]</em>) – kwarg inputs to the module forward function.</p></li>
<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span>
</pre></div>
</div>
</p></li>
<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
<li><p><strong>assume_dynamic_shape_support</strong> (<em>bool</em>) – Setting this to true enables the converters work for both dynamic and static shapes. Default: False</p></li>
<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
<li><p><strong>enabled_precisions</strong> (<em>Union</em><em>[</em><em>Set</em><em>[</em><em>Union</em><em>[</em><em>torch.dpython:type</em><em>, </em><em>dpython:type</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>Union</em><em>[</em><em>torch.dpython:type</em><em>, </em><em>dpython:type</em><em>]</em><em>]</em><em>]</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
<li><p><strong>engine_capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
<li><p><strong>truncate_double</strong> (<em>bool</em>) – Truncate weights provided in double (float64) to float32</p></li>
<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</p></li>
<li><p><strong>min_block_size</strong> (<em>python:int</em>) – The minimum number of contiguous TensorRT convertible operations in order to run a set of operations in TensorRT</p></li>
<li><p><strong>torch_executed_ops</strong> (<em>Optional</em><em>[</em><em>Collection</em><em>[</em><em>Target</em><em>]</em><em>]</em>) – Set of aten operators that must be run in PyTorch. An error will be thrown if this set is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
<li><p><strong>torch_executed_modules</strong> (<em>Optional</em><em>[</em><em>List</em><em>[</em><em>str</em><em>]</em><em>]</em>) – List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
<li><p><strong>pass_through_build_failures</strong> (<em>bool</em>) – Error out if there are issues during compilation (only applicable to torch.compile workflows)</p></li>
<li><p><strong>max_aux_streams</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Maximum streams in the engine</p></li>
<li><p><strong>version_compatible</strong> (<em>bool</em>) – Build the TensorRT engines compatible with future versions of TensorRT (Restrict to lean runtime operators to provide version forward compatibility for the engines)</p></li>
<li><p><strong>optimization_level</strong> – (Optional[int]): Setting a higher optimization level allows TensorRT to spend longer engine building time searching for more optimization options. The resulting engine may have better performance compared to an engine built with a lower optimization level. The default optimization level is 3. Valid values include integers from 0 to the maximum optimization level, which is currently 5. Setting it to be greater than the maximum level results in identical behavior to the maximum level.</p></li>
<li><p><strong>use_python_runtime</strong> – (bool): Return a graph using a pure Python runtime, reduces options for serialization</p></li>
<li><p><strong>use_fast_partitioner</strong> – (bool): Use the adjacency based partitioning scheme instead of the global partitioner. Adjacency partitioning is faster but may not be optimal. Use the global paritioner (<code class="docutils literal notranslate"><span class="pre">False</span></code>) if looking for best performance</p></li>
<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Use the full set of operator decompositions. These decompositions may not be tested but serve to make the graph easier to convert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</p></li>
<li><p><strong>dryrun</strong> (<em>bool</em>) – Toggle for “Dryrun” mode, running everything except conversion to TRT and logging outputs</p></li>
<li><p><strong>hardware_compatible</strong> (<em>bool</em>) – Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)</p></li>
<li><p><strong>timing_cache_path</strong> (<em>str</em>) – Path to the timing cache if it exists (or) where it will be saved after compilation</p></li>
<li><p><strong>lazy_engine_init</strong> (<em>bool</em>) – Defer setting up engines until the compilation of all engines is complete. Can allow larger models with multiple graph breaks to compile but can lead to oversubscription of GPU memory at runtime.</p></li>
<li><p><strong>cache_built_engines</strong> (<em>bool</em>) – Whether to save the compiled TRT engines to storage</p></li>
<li><p><strong>reuse_cached_engines</strong> (<em>bool</em>) – Whether to load the compiled TRT engines from storage</p></li>
<li><p><strong>engine_cache_dir</strong> (<em>str</em>) – Directory to store the cached TRT engines</p></li>
<li><p><strong>engine_cache_size</strong> (<em>python:int</em>) – Maximum hard-disk space (bytes) to use for the engine cache, default is 1GB. If the cache exceeds this size, the oldest engines will be removed by default</p></li>
<li><p><strong>custom_engine_cache</strong> (<em>Optional</em><em>[</em><em>BaseEngineCache</em><em>]</em>) – Engine cache instance to use for saving and loading engines. Users can provide their own engine cache by inheriting from BaseEngineCache. If used, engine_cache_dir and engine_cache_size will be ignored.</p></li>
<li><p><strong>use_explicit_typing</strong> (<em>bool</em>) – This flag enables strong typing in TensorRT compilation which respects the precisions set in the Pytorch model. This is useful when users have mixed precision graphs.</p></li>
<li><p><strong>use_fp32_acc</strong> (<em>bool</em>) – This option inserts cast to FP32 nodes around matmul layers and TensorRT ensures the accumulation of matmul happens in FP32. Use this only when FP16 precision is configured in enabled_precisions.</p></li>
<li><p><strong>refit_identical_engine_weights</strong> (<em>bool</em>) – Refit engines with identical weights. This is useful when the same model is compiled multiple times with different inputs and the weights are the same. This will save time by reusing the same engine for different inputs.</p></li>
<li><p><strong>strip_engine_weights</strong> (<em>bool</em>) – Strip engine weights from the serialized engine. This is useful when the engine is to be deployed in an environment where the weights are not required.</p></li>
<li><p><strong>immutable_weights</strong> (<em>bool</em>) – Build non-refittable engines. This is useful for some layers that are not refittable. If this argument is set to true, <cite>strip_engine_weights</cite> and <cite>refit_identical_engine_weights</cite> will be ignored.</p></li>
<li><p><strong>enable_weight_streaming</strong> (<em>bool</em>) – Enable weight streaming.</p></li>
<li><p><strong>tiling_optimization_level</strong> (<em>str</em>) – The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support [“none”, “fast”, “moderate”, “full”].</p></li>
<li><p><strong>l2_limit_for_tiling</strong> (<em>python:int</em>) – The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).</p></li>
<li><p><strong>offload_module_to_cpu</strong> (<em>bool</em>) – Offload the module to CPU. This is useful when we need to minimize GPU memory usage.</p></li>
<li><p><strong>use_distributed_mode_trace</strong> (<em>bool</em>) – Using aot_autograd to trace the graph. This is enabled when DTensors or distributed tensors are present in distributed model.</p></li>
<li><p><strong>**kwargs</strong> – Any,</p></li>
</ul>
</dd>
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><em>bytes</em></p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.cross_compile_for_windows">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">cross_compile_for_windows</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Any</span></span></span><a class="headerlink" href="#torch_tensorrt.dynamo.cross_compile_for_windows" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.save_cross_compiled_exported_program">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">save_cross_compiled_exported_program</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Any</span></span></span><a class="headerlink" href="#torch_tensorrt.dynamo.save_cross_compiled_exported_program" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.load_cross_compiled_exported_program">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">load_cross_compiled_exported_program</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Any</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_compiler.html#load_cross_compiled_exported_program"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.load_cross_compiled_exported_program" title="Permalink to this definition">¶</a></dt>
<dd><p>Load an ExportedProgram file in Windows which was previously cross compiled in Linux</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>file_path</strong> (<em>str</em>) – Path to file on the disk</p>
</dd>
<dt class="field-even">Raises</dt>
<dd class="field-even"><p><strong>ValueError</strong> – If the api is not called in windows or there is no file or the file is a valid ExportedProgram file</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.refit_module_weights">
<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">refit_module_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Dict</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Any</span></span></span><a class="headerlink" href="#torch_tensorrt.dynamo.refit_module_weights" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

</section>
<section id="classes">
<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this heading">¶</a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="torch_tensorrt.dynamo.CompilationSettings">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">CompilationSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">enabled_precisions:</span> <span class="pre">~typing.Set[~torch_tensorrt._enums.dtype]</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">workspace_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0,</span> <span class="pre">min_block_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">5,</span> <span class="pre">torch_executed_ops:</span> <span class="pre">~typing.Collection[~typing.Union[~collections.abc.Callable[[...],</span> <span class="pre">~typing.Any],</span> <span class="pre">str]]</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">pass_through_build_failures:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">max_aux_streams:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">version_compatible:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">optimization_level:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">use_python_runtime:</span> <span class="pre">~typing.Optional[bool]</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">truncate_double:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">use_fast_partitioner:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">enable_experimental_decompositions:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">device:</span> <span class="pre">~torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">require_full_compilation:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">disable_tf32:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">assume_dynamic_shape_support:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">sparse_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">engine_capability:</span> <span class="pre">~torch_tensorrt._enums.EngineCapability</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">num_avg_timing_iters:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1,</span> <span class="pre">dla_sram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1048576,</span> <span class="pre">dla_local_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1073741824,</span> <span class="pre">dla_global_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">536870912,</span> <span class="pre">dryrun:</span> <span class="pre">~typing.Union[bool,</span> <span class="pre">str]</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">hardware_compatible:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">timing_cache_path:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'/tmp/torch_tensorrt_engine_cache/timing_cache.bin',</span> <span class="pre">lazy_engine_init:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">cache_built_engines:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">reuse_cached_engines:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">use_explicit_typing:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">use_fp32_acc:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">refit_identical_engine_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">strip_engine_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">immutable_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">enable_weight_streaming:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">enable_cross_compile_for_windows:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">tiling_optimization_level:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'none',</span> <span class="pre">l2_limit_for_tiling:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">-1,</span> <span class="pre">use_distributed_mode_trace:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">offload_module_to_cpu:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_settings.html#CompilationSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.CompilationSettings" title="Permalink to this definition">¶</a></dt>
<dd><p>Compilation settings for Torch-TensorRT Dynamo Paths</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>enabled_precisions</strong> (<em>Set</em><em>[</em><em>dpython:type</em><em>]</em>) – Available kernel dtype precisions</p></li>
<li><p><strong>debug</strong> (<em>bool</em>) – Whether to print out verbose debugging information</p></li>
<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Workspace TRT is allowed to use for the module (0 is default)</p></li>
<li><p><strong>min_block_size</strong> (<em>python:int</em>) – Minimum number of operators per TRT-Engine Block</p></li>
<li><p><strong>torch_executed_ops</strong> (<em>Collection</em><em>[</em><em>Target</em><em>]</em>) – Collection of operations to run in Torch, regardless of converter coverage</p></li>
<li><p><strong>pass_through_build_failures</strong> (<em>bool</em>) – Whether to fail on TRT engine build errors (True) or not (False)</p></li>
<li><p><strong>max_aux_streams</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Maximum number of allowed auxiliary TRT streams for each engine</p></li>
<li><p><strong>version_compatible</strong> (<em>bool</em>) – Provide version forward-compatibility for engine plan files</p></li>
<li><p><strong>optimization_level</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Builder optimization 0-5, higher levels imply longer build time,
searching for more optimization options. TRT defaults to 3</p></li>
<li><p><strong>use_python_runtime</strong> (<em>Optional</em><em>[</em><em>bool</em><em>]</em>) – Whether to strictly use Python runtime or C++ runtime. To auto-select a runtime
based on C++ dependency presence (preferentially choosing C++ runtime if available), leave the
argument as None</p></li>
<li><p><strong>truncate_double</strong> (<em>bool</em>) – Whether to truncate float64 TRT engine inputs or weights to float32</p></li>
<li><p><strong>use_fast_partitioner</strong> (<em>bool</em>) – Whether to use the fast or global graph partitioning system</p></li>
<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Whether to enable all core aten decompositions
or only a selected subset of them</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a>) – GPU to compile the model on</p></li>
<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Whether to require the graph is fully compiled in TensorRT.
Only applicable for <cite>ir=”dynamo”</cite>; has no effect for <cite>torch.compile</cite> path</p></li>
<li><p><strong>assume_dynamic_shape_support</strong> (<em>bool</em>) – Setting this to true enables the converters work for both dynamic and static shapes. Default: False</p></li>
<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Whether to disable TF32 computation for TRT layers</p></li>
<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Whether to allow the builder to use sparse weights</p></li>
<li><p><strong>engine_capability</strong> (<em>trt.EngineCapability</em>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
<li><p><strong>dryrun</strong> (<em>Union</em><em>[</em><em>bool</em><em>, </em><em>str</em><em>]</em>) – Toggle “Dryrun” mode, which runs everything through partitioning, short of conversion to
TRT Engines. Prints detailed logs of the graph structure and nature of partitioning. Optionally saves the
output to a file if a string path is specified</p></li>
<li><p><strong>hardware_compatible</strong> (<em>bool</em>) – Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)</p></li>
<li><p><strong>timing_cache_path</strong> (<em>str</em>) – Path to the timing cache if it exists (or) where it will be saved after compilation</p></li>
<li><p><strong>cache_built_engines</strong> (<em>bool</em>) – Whether to save the compiled TRT engines to storage</p></li>
<li><p><strong>reuse_cached_engines</strong> (<em>bool</em>) – Whether to load the compiled TRT engines from storage</p></li>
<li><p><strong>use_strong_typing</strong> (<em>bool</em>) – This flag enables strong typing in TensorRT compilation which respects the precisions set in the Pytorch model. This is useful when users have mixed precision graphs.</p></li>
<li><p><strong>use_fp32_acc</strong> (<em>bool</em>) – This option inserts cast to FP32 nodes around matmul layers and TensorRT ensures the accumulation of matmul happens in FP32. Use this only when FP16 precision is configured in enabled_precisions.</p></li>
<li><p><strong>refit_identical_engine_weights</strong> (<em>bool</em>) – Whether to refit the engine with identical weights</p></li>
<li><p><strong>strip_engine_weights</strong> (<em>bool</em>) – Whether to strip the engine weights</p></li>
<li><p><strong>immutable_weights</strong> (<em>bool</em>) – Build non-refittable engines. This is useful for some layers that are not refittable. If this argument is set to true, <cite>strip_engine_weights</cite> and <cite>refit_identical_engine_weights</cite> will be ignored</p></li>
<li><p><strong>enable_weight_streaming</strong> (<em>bool</em>) – Enable weight streaming.</p></li>
<li><p><strong>enable_cross_compile_for_windows</strong> (<em>bool</em>) – By default this is False means TensorRT engines can only be executed on the same platform where they were built.
True will enable cross-platform compatibility which allows the engine to be built on Linux and run on Windows</p></li>
<li><p><strong>tiling_optimization_level</strong> (<em>str</em>) – The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support [“none”, “fast”, “moderate”, “full”].</p></li>
<li><p><strong>l2_limit_for_tiling</strong> (<em>python:int</em>) – The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).</p></li>
<li><p><strong>use_distributed_mode_trace</strong> (<em>bool</em>) – Using aot_autograd to trace the graph. This is enabled when DTensors or distributed tensors are present in distributed model</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</section>
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